markdown anchor tags and links, for AI code

"Markdown" files are becoming a primary format for "AI enabled coding" workflows.

Also for documenting what is done.

Usually there are many files, some could be large, so being able to reference to part of the file is useful.

Since Markdown is based on HTML, anchor tags are supported, and even easier to use.

Learn how Markdown Links work. Welcome to the world of Markdown Links… | by Jochen Gererstorfer | inhalt | Medium


html - How to set an anchor in Markdown File - Stack Overflow

Named Anchors & Markdown - DEV Community

AI Overview

Anchor tags in Markdown allow you to link directly to a specific section within another file by combining the file path with a heading ID (anchor). 

How it Works

1. Identify the Anchor: Most Markdown renderers (like GitHub or VS Code) automatically generate anchors for headings by converting the text to lowercase and replacing spaces with hyphens.
2. Combine Path and Anchor: To link to a section in a different file, use the format: [Link Text](path/to/file.md#anchor-name)

Assume you have two files in the same folder: README.md and setup.md. 

In setup.md:

markdown

# Installation Guide

## Step 2: Configuration
This is the section we want to link to.

The auto-generated anchor for the heading above is #step-2-configuration. 

In README.md:
To link to that specific section from your main file:

markdown

Check out the [Configuration Section](setup.md#step-2-configuration) for more details.

Advanced Usage

• Custom Anchors: If your renderer doesn't auto-generate IDs or you want a shorter link, you can manually insert an HTML anchor tag before a heading: <a id="custom-id"></a>. Link to it using setup.md#custom-id.
• Case Sensitivity: Most platforms require the anchor part (after the #) to be all lowercase.
• Special Characters: Punctuation in headings is typically ignored when generating the ID (e.g., ## Setup! becomes #setup)

Tesla’s Solar, $2.9 Billion investment

VERY smart and useful move! Bravo Tesla!

Good for everybody, since demand for energy is much more than supply,
plus good for environment, and the most affordable energy. 

And this is bringing manufacturing to US, and not affected by taxes and tarrifs.

Tesla’s $2.9 Billion Solar Bet Could Change America Forever - YouTube

Tesla is reportedly investing $2.9 billion in Chinese manufacturing equipment
to dramatically expand its solar production capabilities in the United States. 

The goal is to establish 100 gigawatts per year of solar manufacturing capacity in Texas by 2028, aiming to build a vertically integrated supply chain from raw materials to finished panels. 

This move is designed to disrupt the expensive U.S. solar market, improve domestic energy independence, and support rising electricity demands from Tesla operations, SpaceX, and data centers.

Semantic Web: SHACL: Shapes Constraint Language

In the time of AI LLMs, "classic" content management tools and techniques look obsolete.
But they are not, just not mainstream, because they are complex/not easy.

In fact, maybe by leveraging AI tools such tools and languages may actually be more useful.

Same as complex programming languages, like Rust, that can now be usable with more people.

SHACL - Wikipedia

Shapes Constraint Language^[1] (SHACL) is a World Wide Web Consortium (W3C) standard language for describing Resource Description Framework (RDF) graphs. SHACL has been designed to enhance the semantic and technical interoperability layers of ontologies expressed as RDF graphs.^[3]

SHACL models are defined in terms of constraints on the content, structure and meaning of a graph. SHACL is a highly expressive language. Among others, it includes features to express conditions that constrain the number of values that a property may have, the type of such values, numeric ranges, string matching patterns, and logical combinations of such constraints. SHACL also includes an extension mechanism to express more complex conditions in languages such as SPARQL and JavaScript. SHACL Rules add inferencing capabilities to SHACL, allowing users to define what new statements can be inferred from existing (asserted) statements.

Status Published, W3C Recommendation[1]

W3C Recommendation 20 July 2017

What Is SHACL | Ontotext Fundamentals

kurtcagle/shaclify: A set of open source routines for converting between different data representations using the W3C SHACL specification and AI generation. @GitHub